The volcano3D package enables exploration of probes differentially expressed between three groups. Its main purpose is for the visualisation of differentially expressed genes in a three-dimensional volcano plot. These plots can be converted to interactive visualisations using plotly.
This vignette will consist of a number of case studies focusing on the PEAC rheumatoid arthritis study (Pathobiology of Early Arthritis Cohort). The methodology has been published in ‘Lewis, Myles J., et al. “Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes.” Cell reports 28.9 (2019): 2455-2470.’ with an interactive web tool available at https://peac.hpc.qmul.ac.uk.
Not yet publicly available:
install.packages("volcano3D")
Not yet publicly available:
library(devtools)
install_github("KatrionaGoldmann/volcano3D")
library(volcano3D)
Variables used in this vignette:
| Variable | Definition |
|---|---|
| contrast | the variable by which samples can be split into three groups. |
| groups | the three levels/categories of the contrast variable. |
| comparison | two groups between which a statistical test can be performed. There are three comparisons total. For the examples outlined in this vignette we look at comparisons: ‘lymphoid-myeloid’, ‘lymphoid-fibroid’ and ‘myeloid-fibroid’. |
| p | p value |
| FC | fold change |
| padj | adjusted p value |
| suffix | the tail word in a column name. In this package it states the statistical parameter (e.g. _logFC is the log FC variable) |
| prefix | the leading word in a column name. In this package it states the statistical test (e.g. LRT is the likelihood ratio test). |
| dep | ‘Differential Expression P-values’ object, of S4 class, containing the expression data, sample data and pvalues. |
This vignette will demonstrate the power of this package using a basic example from the PEAC data set. This analysis is separated by biopsy/tissue type: blood and synovium. Here we will focus on the synovial data.
First we will set up a differential expression pvalues object (dep) containing:
To create a dep object you must provide the ‘create_dep’ function with:
sampledata: A data frame containing the sample information. This must contain:
ID column: Containing the sample IDs. This must be titled ‘ID’.contrast column: A column containing the three-level factor used for contrasts.contrast: The column name in sampledata which contains the three-level factor used for contrast.pvalues: A data frame containing:
paste0(groups[i], "-", groups[j], " ", pColSuffix)). We recommend using limma or DESeq pipelines to calculate these pvalues for gene expression.paste(multi_group_prefix, p_col_suffix)). This is typically generated using ANOVA or likelihood ratio tests between all three groups.paste0(groups[i], "-", groups[j], " ", fcColSuffix))paste(multi_group_prefix, fc_col_suffix)). This is typically generated using ANOVA or likelihood ratio tests comparing all three groups.paste0(groups[i], "-", groups[j], " ", padj_col_suffix))paste(multiGroupPrefix, padjColSuffix)). This is typically generated using ANOVA or likelihood ratio tests comparing all three groups.with optional:
groups: The groups to be compared (in order). If NULL this defaults to levels(sampledata[, 'contrasts']).p_col_suffix: The suffix of column names with pvalues (default is ‘pvalue’).padj_col_suffix: The suffix of column names with adjusted pvalues (default is ‘padj’). If NULL the adjusted pvalue is calculated using p_col_suffix and pvalue_method.padjust_method: The method to calculate adjusted pvalues if not already provided. Must be one of c(‘holm’, ‘hochberg’, ‘hommel’, ‘bonferroni’, ‘BH’, ‘BY’, ‘fdr’, ‘none’). Default is ‘BH’.fc_col_suffix: The suffix of column names with log(fold change) values (default is ‘logFC’).multi_group_prefix: The prefix for columns containing statistics for a multi-group test (this is typically a likelihood ratio test or ANOVA). Default is NULL.Using the synovial biopsies from PEAC we can create a dep object for differentially expressed genes
library(volcano3Ddata)
data("syn_data")
syn_p_obj <- create_dep(sampledata = syn_metadata,
contrast = "Pathotype",
pvalues = syn_pvalues,
p_col_suffix = "pvalue",
fc_col_suffix = "log2FoldChange",
multi_group_prefix = "LRT",
expression = syn_rld)
The pvalues slot should have three statistics for each comparison: pvalue, adjusted pvalue and logarithmic fold change:
head(syn_p_obj@pvalues) %>%
kable() %>%
kable_styling(font_size=8.7)
| Fibroid-Lymphoid logFC | Fibroid-Lymphoid padj | Fibroid-Lymphoid pvalue | LRT logFC | LRT padj | LRT pvalue | Lymphoid-Myeloid logFC | Lymphoid-Myeloid padj | Lymphoid-Myeloid pvalue | Myeloid-Fibroid logFC | Myeloid-Fibroid padj | Myeloid-Fibroid pvalue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A2M | -0.0478326 | 1 | 0.7775361 | 0.2681094 | 0.4353436 | 0.2739571 | -0.2202768 | 1.0000000 | 0.1563045 | 0.2681094 | 1 | 0.1694654 |
| A2ML1 | 1.6854024 | 1 | 0.0002106 | 0.2445664 | 0.0000202 | 0.0000022 | -1.9299687 | 0.0464724 | 0.0000030 | 0.2445664 | 1 | 0.6348844 |
| A4GALT | 1.0248506 | 0 | 0.0000000 | -0.4601841 | 0.0000000 | 0.0000000 | -0.5646665 | 0.2927492 | 0.0000192 | -0.4601841 | 1 | 0.0055222 |
| A4GNT | -1.1527859 | 1 | 0.0238302 | 1.3385294 | 0.1583022 | 0.0732984 | -0.1857435 | 1.0000000 | 0.6691773 | 1.3385294 | 1 | 0.0197330 |
| AAAS | -0.0245296 | 1 | 0.8574281 | -0.0303402 | 0.9711813 | 0.9075226 | 0.0548697 | 1.0000000 | 0.6608837 | -0.0303402 | 1 | 0.8469982 |
| AACS | 0.1729592 | 1 | 0.2815302 | 0.1002448 | 0.2732939 | 0.1472059 | -0.2732040 | 1.0000000 | 0.0630182 | 0.1002448 | 1 | 0.5874802 |
We can now investigate the comparisons between pathotypes using the volcano_trio function. This creates three ggplot outputs.
syn_plots <- volcano_trio(dep = syn_p_obj,
sig_names = c("not significant","significant",
"not significant","significant"),
colours = rep(c("grey60", "salmon"), 2),
text_size = 9,
shared_legend_size = 0.9,
label_rows = c("SLAMF6", "PARP16", "ITM2C"),
fc_line = FALSE)
syn_plots$All
Next the coordinates and colours to map each variable, or row, in the expression/pvalues data onto a polar coordinate system is calculated using the polar_coords function:
syn_polar <- polar_coords(dep = syn_p_obj)
The sig column in syn_polar@polar allows us to determine relative differences in expression between pathotypes. The ‘+’ indicates which pathotypes are significantly ‘up’ compared to others. For example:
primary_colours variable in polar_coords.secondary_colours.not_sig_colour.table(syn_polar@polar$sig) %>%
kable(col.names = c("Significance", "Frequency")) %>%
kable_styling(full_width = F)
| Significance | Frequency |
|---|---|
| Fibroid | 885 |
| Fibroid+Lymphoid+ | 19 |
| Fibroid+Myeloid+ | 1504 |
| Lymphoid | 1793 |
| Lymphoid+Myeloid+ | 500 |
| Myeloid | 119 |
| Not Significant | 11415 |
These can be output to an interactive radar plot using radial_plotly. The labelRows variable allows any markers of interest to be labelled.
radial_plotly(polar = syn_polar,
fc_cutoff = 0.1,
label_rows = c("SLAMF6", "PARP16", "ITM2C"))
By hovering over certain point you can also determine genes for future interrogation.
Similarly we can create a static ggplot image using radial_ggplot:
radial_ggplot(polar = syn_polar,
fc_cutoff = 0.1,
label_rows = c("SLAMF6", "PARP16", "ITM2C"),
marker_size = 2.3,
legend_size = 10) +
theme(legend.position = "right")
We can then interrogate any one specific variable as a boxplot, to investigate these differences. This is build using ggplot2 so can easily be edited by the user to add features.
plot1 <- boxplot_trio(syn_p_obj,
value = "SLAMF6",
test = "wilcox.test",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
box_colours = c("blue", "red", "green3"))
plot2 <- boxplot_trio(syn_p_obj,
value = "PARP16",
test = "wilcox.test",
levels_order = c("Myeloid", "Fibroid"),
box_colours = c("red", "green3"))
plot3 <- boxplot_trio(syn_p_obj,
value = "ITM2C",
test = "wilcox.test",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
my_comparisons = list(c("Lymphoid", "Myeloid"),
c("Myeloid", "Fibroid")),
box_colours = c("blue", "red", "green3"))
ggarrange(plot1, plot2, plot3, ncol = 3)
The final thing we can look at is the 3D volcano plot which projects differential gene expression onto cylindrical coordinates.
p <- volcano3D(syn_polar,
label_rows = c("SLAMF6", "PARP16", "ITM2C"),
label_size = 10,
xy_aspectratio = 1,
z_aspectratio = 0.9)
p %>% layout(legend = list(x = 100, y = 0.5))
Again this produces an interactive plot. If you have the orca command-line utility installed, this can be used to save static images. To install follow the instructions here.
orca(p, "./volcano_3d_synovium.svg", format = "svg")
We can then collapse this into a modular analysis using a list of gene sets. In this example we have used the blood transcript modules curated by Li et. al. in ‘Li, S., Rouphael, N., Duraisingham, S., Romero-Steiner, S., Presnell, S., Davis, C., … & Kasturi, S. (2014). Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nature immunology, 15(2), 195.’. The pvalues were generated using QuSAGE methodology.
library(volcano3Ddata)
data("Li_pvalues")
syn_mod_p_obj <- create_dep(sampledata = syn_metadata,
contrast = "Pathotype",
pvalues = syn_mod_pvalues,
p_col_suffix = "p.value",
padj_col_suffix = "q.value",
fc_col_suffix = "logFC",
multi_group_prefix = NULL,
expression = syn_mod)
We can now investigate the comparisons between pathotypes using the volcano_trio function.
syn_mod_plots <- volcano_trio(dep = syn_mod_p_obj,
label_rows = c("M156.0", "M37.2"),
shared_legend_size = 1,
sig_names = c("Not Sig",
paste("Padj <", 0.05),
paste("|FC| >", 1),
paste("Padj <", 0.05,
"&\n|FC| >", 1)))
syn_mod_plots$All
Next the coordinates and colours to map each variable, or row, in the expression/pvalues data onto a polar coordinate system is calculated using the polar_coords function:
syn_polar <- polar_coords(dep = syn_mod_p_obj, significance_cutoff = 0.01)
table(syn_polar@polar$sig) %>%
kable(col.names = c("Significance", "Frequency")) %>%
kable_styling(full_width = F)
| Significance | Frequency |
|---|---|
| Fibroid | 15 |
| Fibroid+Myeloid+ | 43 |
| Lymphoid | 101 |
| Lymphoid+Myeloid+ | 65 |
| Myeloid | 9 |
| Not Significant | 112 |
These can be output to an interactive radar plot using radial_plotly. The labelRows variable allows any markers of interest to be labelled.
radial_plotly(polar = syn_polar,
fc_cutoff = 0.3,
label_rows = c("M156.0", "M37.2")) %>%
layout(width = 650, height = 650)
Or a ggplot static image using radial_ggplot:
radial_ggplot(polar = syn_polar,
fc_cutoff = 0.1,
label_rows = c("M156.0", "M37.2"),
marker_size = 2.7,
label_size = 5,
axis_lab_size = 3,
axis_title_size = 5,
legend_size = 10) +
theme(legend.position = "right")
We can then interrogate any one specific variable as a boxplot, to investigate these differences.
plot1 <- boxplot_trio(syn_mod_p_obj,
value = "M156.0",
test = "wilcox.test",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
box_colours = c("blue", "red", "green3"))
plot2 <- boxplot_trio(syn_mod_p_obj,
value = "M37.2",
test = "wilcox.test",
levels_order = c("Lymphoid", "Myeloid", "Fibroid"),
box_colours = c("blue", "red", "green3"))
ggarrange(plot1, plot2)
If you use this package please cite as:
Lewis, Myles J., et al. “Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes.” Cell reports 28.9 (2019): 2455-2470.
or using:
citation("volcano3D")
##
## To cite package 'volcano3D' in publications use:
##
## Katriona Goldmann and Myles Lewis (2020). volcano3D: Interactive
## Plotting of Three-Way Differential Expression Analysis. R package
## version 0.1.0.9000. https://github.com/KatrionaGoldmann/volcano3D
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {volcano3D: Interactive Plotting of Three-Way Differential Expression
## Analysis},
## author = {Katriona Goldmann and Myles Lewis},
## year = {2020},
## note = {R package version 0.1.0.9000},
## url = {https://github.com/KatrionaGoldmann/volcano3D},
## }
##
## ATTENTION: This citation information has been auto-generated from the
## package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.